Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators.
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Data Science with NumPy, Sets, and Dictionaries
Instructeurs : Genevieve M. Lipp
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Compétences que vous acquerrez
- Catégorie : Data Analysis
- Catégorie : Python Programming
- Catégorie : Numpy
- Catégorie : Object-Oriented Programming (OOP)
- Catégorie : Arrays
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Il y a 4 modules dans ce cours
This module, you will learn the basics of object oriented programming as well as how to use sets and dictionaries to store and work with data in Python. You will apply these concepts with Python to perform some mathematical operations and analytical tasks, including solving geometric problems with circles and counting words in a document.
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10 vidéos4 lectures4 devoirs de programmation
This module, you will learn how to utilize NumPy--one of the most useful Python packages we use in data science--as well as learn additional data structures, arrays, beginning with the simplest type of an array, a vector. With NumPy and your new understanding of vectors, you will develop histograms as well as analyze household income distribution data in the United States, drawing your own data-driven conclusions.
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1 vidéo9 lectures2 quizzes3 laboratoires non notés
This module, you will first learn how NumPy handles data in your program using views and copies of your data. You will then learn how to work with more complex arrays called matrices, as well as how you can subset, filter, and modify data in matrices. Finally, you will write your own programs to manipulate data matrices and report your results for a given dataset.
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1 vidéo14 lectures1 quiz3 laboratoires non notés
In this module, you will learn how to use NumPy to summarize data from matrices (e.g., calculating averages, minimums, maximums, etc.) as well as how to begin to analyze and manipulate image data. You will also explore two new data science techniques: how to make your analysis of data matrices more computationally efficient (vectorization) and how to randomize data (randomization).
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1 vidéo11 lectures1 quiz2 laboratoires non notés
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